arXiv research

The paper analyzes how mutable blockchain protocols affect miner behavior and strategic stability.

We introduce a strategic behavior in reinsurance bilateral transactions, where agents choose the risk preferences they will appear to have in the transaction. Within a wide class of risk measures, we identify agents' strategic choices to a range of risk aversion coefficients. It is shown that at the strictly beneficial…

Paper generalizes strategic classification framework and introduces SVC for PAC-learning.

Ad exchanges use CORP to set reserve prices against strategic buyers.

The results of a learning process depend on the input data. There are cases in which an adversary can strategically tamper with the input data to affect the outcome of the learning process. While some datasets are difficult to attack, many others are susceptible to manipulation. A resourceful attacker can tamper with l…

The dynamics of many socioeconomic systems is determined by the decision making process of agents. The decision process depends on agent's characteristics, such as preferences, risk aversion, behavioral biases, etc.. In addition, in some systems the size of agents can be highly heterogeneous leading to very different i…

Method distills reward and strategies from diverse demonstrators.

We introduce two tactics to attack agents trained by deep reinforcement learning algorithms using adversarial examples, namely the strategically-timed attack and the enchanting attack. In the strategically-timed attack, the adversary aims at minimizing the agent's reward by only attacking the agent at a small subset of…

We consider thin incomplete financial markets, where traders with heterogeneous preferences and risk exposures have motive to behave strategically regarding the demand schedules they submit, thereby impacting prices and allocations. We argue that traders relatively more exposed to market risk tend to submit more elasti…

The game theory techniques are used to find the equilibrium of a market. Game theory refers to the ways in which strategic interactions among economic agents produce outcomes with respect to the preferences (or utilities) of those agents, where the outcomes in question might have been intended by none of the agents. Th…

Study analyzes market equilibrium returns with price impact and transaction costs.

Active IRL selects optimal human demonstrations for learning AI preferences.

Autonomous systems can substantially enhance a human's efficiency and effectiveness in complex environments. Machines, however, are often unable to observe the preferences of the humans that they serve. Despite the fact that the human's and machine's objectives are aligned, asymmetric information, along with heterogene…

Private anchors affect how information is communicated and can improve or distort transmission.

New voting rules protect against strategic voting by robust statistics.

This paper develops a learning framework for optimal strategies in multi-stage decentralized matching markets.

Paper addresses RLHF alignment challenges with novel algorithms.

As the cornerstone of modern portfolio theory, Markowitz's mean-variance optimization is considered a major model adopted in portfolio management. However, due to the difficulty of estimating its parameters, it cannot be applied to all periods. In some cases, naive strategies such as Equally-weighted and Value-weighted…

Study shows marketable order routing to wholesalers benefits all traders, leading to lower market depth and price volatility.

We quantify content availability and user discovery opportunities in recommender systems.

New framework for robust uncertainty quantification in strategic settings.

Study strategic dynamic pricing for buyers with unknown manipulation costs.

New framework reduces strategic manipulation cost for minority groups in fair classification.

Study compares employers with and without anticipating strategic labor force responses.

New algorithm learns optimal policies in strategic MDPs with private types.

Deep neural network (DNN) has demonstrated its success in multiple domains. However, DNN models are inherently vulnerable to adversarial examples, which are generated by adding adversarial perturbations to benign inputs to fool the DNN model to misclassify. In this paper, we present a cross-layer strategic ensemble fra…

New findings show strategic interactions can undermine model expressiveness in machine learning.

Consequential decision-making typically incentivizes individuals to behave strategically, tailoring their behavior to the specifics of the decision rule. A long line of work has therefore sought to counteract strategic behavior by designing more conservative decision boundaries in an effort to increase robustness to th…

Strategic feature selection in high-stakes domains like healthcare.

Study allocates resources to strategic agents while balancing cost and incentives.

The paper analyzes trading strategies in a competitive market with incomplete information.

Algorithm learns optimal coordination for strategic agents in uncertain settings.

Randomised classifiers outperform deterministic ones in strategic classification.

Study one-shot strategic classification under unknown costs, improving worst-case accuracy.

Consequential decision-making incentivizes individuals to strategically adapt their behavior to the specifics of the decision rule. While a long line of work has viewed strategic adaptation as gaming and attempted to mitigate its effects, recent work has instead sought to design classifiers that incentivize individuals…

We consider a symmetric multi-players zero-sum game with two strategic variables. There are $n$ players, $n\geq 3$. Each player is denoted by $i$. Two strategic variables are $t_i$ and $s_i$, $i\in \{1, \dots, n\}$. They are related by invertible functions. Using the minimax theorem by \cite{sion} we will show that Nas…

We study a strategic version of the multi-armed bandit problem, where each arm is an individual strategic agent and we, the principal, pull one arm each round. When pulled, the arm receives some private reward $v_a$ and can choose an amount $x_a$ to pass on to the principal (keeping $v_a-x_a$ for itself). All non-pulle…

COBRA addresses strategic behavior in online platforms by ensuring truthful reporting without monetary incentives.

Strategic feature manipulation helps learners identify meaningful variables in online regression settings.

We study revenue optimization learning algorithms for repeated second-price auctions with reserve where a seller interacts with multiple strategic bidders each of which holds a fixed private valuation for a good and seeks to maximize his expected future cumulative discounted surplus. We propose a novel algorithm that h…

This paper deals with the explicit design of strategy formulations to make the best strategic choices from a conventional matrix form of representing strategic choices. The explicit strategy formulation is an analytical model which is targeted to provide a mathematical strategy framework to find the best moment for str…

Market-maker optimizes quotes based on strategic market-takers' behavior.

The paper tackles performative policy learning with strategic agents, improving scalability and generalizability.

Strategic brokers exploit private information in broker-mediated markets, affecting informed traders' performance.

This paper tackles online strategic decision making with asymmetry and knowledge transportability.

User strategization undermines algorithmic trustworthiness.

New algorithms reduce matching regret by limiting frequent updates.

We study revenue optimization learning algorithms for repeated posted-price auctions where a seller interacts with a single strategic buyer that holds a fixed private valuation for a good and seeks to maximize his cumulative discounted surplus. For this setting, first, we propose a novel algorithm that never decreases …